At present, sentiment analysis has become a trend; above all, in digital product development companies, as it is essential for rapid and automatic analysis. Sentiment analysis deals with emotions with the help of software, and it is playing an unavoidable role in workplaces. The constant growth of social networks, especially the Twitter social network, has made the ability to understand and comprehend users or clients take a greater scope regarding their needs; and therefore, increase the complexity of analysis of this social network, causing excessive expenses in time, personnel and money. This work presents a solution through the application of Machine Learning (ML) for sentiment analysis and thus improve analysis, execution time and customer satisfaction. The scope of this research is limited to using the Support Vector Machine (SVM) supervised learning technique for the intended anal...

Faced with Covid-19, and the need to adapt to environments that guarantee continuity of educational service in the context of social distancing, many universities did not initially plan the mechanisms for adapting to the virtual modality adequately. Therefore, this period of transition to e-learning was characterised by a decrease in academic performance . This article reports on a study that focused on determining whether the transition from a classroom to a virtual teaching–learning model had an effect or influence on the academic performance of university students in mechanical and electrical engineering at a public university in Peru during the period 2018 to 2021. The purpose of the study was to ensure the quality of the education system in the face of the implementation of a hybrid mode of teaching. Methodologically, a descriptive type of investigation and longitudinal non-experi...

The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the h...